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Learning and teaching requires the transfer of knowledge from one person to another. Due to the relevance of knowledge many models have been developed for knowledge transfer. However, the process of knowledge transfer has not yet been described completely and the approaches are too vague to facilitate its implementation. This paper contributes to a better understanding of knowledge transfer to support knowledge transfer in teaching. To address this challenge, we depict a layered model for knowledge transfer. The model structures the transfer in several steps and thus identifies major influencing factors. The paper describes the knowledge transfer from one person to another step by step. An example in the area of teaching business process management illuminates the process. The main contribution of this paper is the development of a layered model and its application in teaching.
Business processes are important knowledge resources of a company. The knowledge contained in business processes impart procedures used to create products and services. However, modelling and application of business processes are affected by problems connected to knowledge transfer. This paper presents and implements a layered model to improve the knowledge transfer. Thus modelling and understanding of business process models is supported. An evaluation of the approach is presented and results and other areas of application are discussed.
A sequence of transactions represents a complex and multi dimensional type of data. Feature construction can be used to reduce the data´s dimensionality to find behavioural patterns within such sequences. The patterns can be expressed using the blue prints of the constructed relevant features. These blue prints can then be used for real time classification on other sequences.
The recent years and especially the Internet have changed the way on how data is stored. We now often store data together with its creation time-stamp. These data sequences potentially enable us to track the change of data over time. This is quite interesting, especially in the e-commerce area, in which classification of a sequence of customer actions, is still a challenging task for data miners. However, before Standard algorithms such as Decision Trees, Neuronal Nets, Naive Bayes or Bayesian Belief Networks can be applied on sequential data, preparations need to be done in order to capture the information stored within the sequences. Therefore, this work presents a systematic approach on how to reveal sequence patterns among data and how to construct powerful features out of the primitive sequence attributes. This is achieved by sequence aggregation and the incorporation of time dimension into the Feature construction step. The proposed algorithm is described in detail and applied on a real life data set, which demonstrates the ability of the proposed algorithm to boost the classification performance of well known data mining algorithms for classification tasks.
When forecasting sales figures, not only the sales history but also the future price of a product will influence the sales quantity. At first sight, multivariate time series seem to be the appropriate model for this task. Nontheless, in real life history is not always repeatable, i.e. in the case of sales history there is only one price for a product at a given time. This complicates the design of a multivariate time series. However, for some seasonal or perishable products the price is rather a function of the expiration date than of the sales history. This additional information can help to design a more accurate and causal time series model. The proposed solution uses an univariate time series model but takes the price of a product as a parameter that influences systematically the prediction. The price influence is computed based on historical sales data using correlation analysis and adjustable price ranges to identify products with comparable history. Compared to other techniques this novel approach is easy to compute and allows to preset the price parameter for predictions and simulations. Tests with data from the Data Mining Cup 2012 demonstrate better results than established sophisticated time series methods.
This work presents a disconnected transaction model able to cope with the increased complexity of longliving, hierarchically structured, and disconnected transactions. Wecombine an Open and Closed Nested Transaction Model with Optimistic Concurrency Control and interrelate flat transactions with the aforementioned complex nature. Despite temporary inconsistencies during a transaction’s execution our model ensures consistency.
This paper presents a concurrency control mechanism that does not follow a ‘one concurrency control mechanism fits all needs’ strategy. With the presented mechanism a transaction runs under several concurrency control mechanisms and the appropriate one is chosen based on the accessed data. For this purpose, the data is divided into four classes based on its access type and usage (semantics). Class O (the optimistic class) implements a first-committer-wins strategy, class R (the reconciliation class) implements a first-n-committers-win strategy, class P (the pessimistic class) implements a first reader-wins strategy, and class E (the escrow class) implements a firsnreaderswin strategy. Accordingly, the model is called OjRjPjE. Under this model the TPC-C benchmark outperforms other CC mechanisms like optimistic Snapshot Isolation.
Transaction processing is of growing importance for mobile computing. Booking tickets, flight reservation, banking, ePayment, and booking holiday arrangements are just a few examples for mobile transactions. Due to temporarily disconnected situations the synchronisation and consistent transaction processing are key issues. Serializability is a too strong criteria for correctness when the semantics of a transaction is known. We introduce a transaction model that allows higher concurrency for a certain class of transactions defined by its semantic. The transaction results are ”escrow serializable” and the synchronisation mechanism is non-blocking. Experimental implementation showed higher concurrency, transaction throughput, and less resources used than common locking or optimistic protocols.
Modern web-based applications are often built as multi-tier architecture using persistence middleware. Middleware technology providers recommend the use of Optimistic Concurrency Control (OCC) mechanism to avoid the risk of blocked resources. However, most vendors of relational database management systems implement only locking schemes for concurrency control. As consequence a kind of OCC has to be implemented at client or middleware side.
A simple Row Version Verification (RVV) mechanism has been proposed to implement an OCC at client side. For performance reasons the middleware uses buffers (cache) of its own to avoid network traffic and possible disk I/O. This caching however complicates the use of RVV because the data in the middleware cache may be stale (outdated). We investigate various data access technologies, including the new Java Persistence API (JPA) and Microsoft’s LINQ technologies for their ability to use the RVV programming discipline.
The use of persistence middleware that tries to relieve the programmer from the low level transaction programming turns out to even complicate the situation in some cases.Programmed examples show how to use SQL data access patterns to solve the problem.
In this presentation the audience will be: (a) introduced to the aims and objectives of the DBTechNet initiative, (b) briefed on the DBTech EXT virtual laboratory workshops (VLW), i.e. the educational and training (E&T) content which is freely available over the internet and includes vendor-neutral hands-on laboratory training sessions on key database technology topics, and (c) informed on some of the practical problems encountered and the way they have been addressed. Last but not least, the audience will be invited to consider incorporating some or all of the DBTech EXT VLW content into their higher education (HE), vocational education and training (VET), and/or lifelong learning/training type course curricula. This will come at no cost and no commitment on behalf of the teacher/trainer; the latter is only expected to provide his/her feedback on the pedagogical value and the quality of the E&T content received/used.
